Systematization of Corner Cases for Visual Perception in Automated Driving

One major task in automated driving is the development of robust and safe visual perception modules. It is of utmost importance that visual perception reacts adequately to so-called corner cases, which range from overexposure of the image sensor to unexpected and potentially dangerous traffic situations. Their detection thus has high significance both as an online system in the intelligent vehicle, but also in the extraction of relevant training and test data for perception modules. In this paper, we provide a systematization of corner cases for visual perception in automated driving, with the categories being structured by detection complexity. Furthermore, we discuss existing metrics and datasets which can be used for the evaluation of corner case detection methods depending on their suitability to provide beneficial information for the various categories.